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Review

Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning

by
Yaseen Ahmed Mohammed Alsumaidaee
1,
Chong Tak Yaw
2,*,
Siaw Paw Koh
2,3,*,
Sieh Kiong Tiong
2,3,
Chai Phing Chen
3 and
Kharudin Ali
4
1
College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
2
Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
3
Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia
4
Faculty of Electrical and Automation Engineering Technology, UC TATI, Teluk Kalong, Kemaman 24000, Terengganu, Malaysia
*
Authors to whom correspondence should be addressed.
Energies 2022, 15(18), 6762; https://doi.org/10.3390/en15186762
Submission received: 5 July 2022 / Revised: 15 August 2022 / Accepted: 19 August 2022 / Published: 15 September 2022

Abstract

In power energy distribution networks, switchgear is considered critical equipment. This is because the act of monitoring the operation and condition of switchgear, as well as performing the required corrective maintenance on any potentially problematic equipment, is critical. A single event may harm thousands of customers over time and pose a significant risk to operational staff. Many considerations must be put in place before using outages to switch down the system since they may raise maintenance costs and disrupt the power supply to users. As a result, proper interpretation of switchgear status evaluations is critical for the early identification of possible faults. Existing ultrasound condition-based monitoring (CBM) diagnostic testing techniques require the tester to manually interpret test data. This study aims to review the status of the recent development of CBM techniques with faults in switchgear. The current trend in electrification will be toward the safety and sustainability of power grid assets, which involves an evaluation of the electrical systems’ and components’ health and grids for medium-voltage distribution. This work provides a current state-of-the-art evaluation of deep learning (DL)-based smart diagnostics that were used to identify partial discharges and localize them. DL techniques are discussed and categorized, with special attention given to those sophisticated in the last five years. The key features of each method, such as fundamental approach and accuracy, are outlined and compared in depth. The benefits and drawbacks of various DL algorithms are also examined. The technological constraints that hinder sophisticated PD diagnostics from being implemented in companies are also recognized. Lastly, various remedies are suggested, as well as future research prospects.
Keywords: arcing; condition-based monitoring; deep learning; fault detection; medium voltage; partial discharge; switchgear arcing; condition-based monitoring; deep learning; fault detection; medium voltage; partial discharge; switchgear

Share and Cite

MDPI and ACS Style

Alsumaidaee, Y.A.M.; Yaw, C.T.; Koh, S.P.; Tiong, S.K.; Chen, C.P.; Ali, K. Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning. Energies 2022, 15, 6762. https://doi.org/10.3390/en15186762

AMA Style

Alsumaidaee YAM, Yaw CT, Koh SP, Tiong SK, Chen CP, Ali K. Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning. Energies. 2022; 15(18):6762. https://doi.org/10.3390/en15186762

Chicago/Turabian Style

Alsumaidaee, Yaseen Ahmed Mohammed, Chong Tak Yaw, Siaw Paw Koh, Sieh Kiong Tiong, Chai Phing Chen, and Kharudin Ali. 2022. "Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning" Energies 15, no. 18: 6762. https://doi.org/10.3390/en15186762

APA Style

Alsumaidaee, Y. A. M., Yaw, C. T., Koh, S. P., Tiong, S. K., Chen, C. P., & Ali, K. (2022). Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning. Energies, 15(18), 6762. https://doi.org/10.3390/en15186762

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